
Cleaning techniques:

Lower casing of words [1]}.


Classification features:

Character n-gram weighted by TF-IDF: Handles the case of spelling mistakes and spelling variations like 'kill yrslef'. It has been found to be more effective than token based n-grams [2]}, [1]}.

Frequency of URL mentions and punctuation comment and token lengths, capitalization, words that cannot be found in English dictionaries, and the number of non-alpha numeric characters present in tokens [2]}.

Word generalization [2]}

Sentiment analysis using PMI based approach [6]}, word list approach [2]}. Further emoticons, punctuations and elongated words (e.g. 'Ohhhh') can be used [6]}.

Part of Speech (POS) tags [2]}, [1]}

Offensiveness level score: Compute a score based on co-occurrences of offensive words in a range of distance [2]}.

Knowledge based approach: Use pre-constructed semantically connected network of words to detect offensive tweet [2]}.

Lexical feature: Use a word list to identify hate/offensive words [6]}.


Classifiers:
The supervised learning approach has been predominately used and widely suggested for classification. The Support Vector Machine (SVM) classifier performs better among classifiers Recent papers suggest using Deep Learning approach but we ruled it out as it is out of scope for this course.[2]}.

